Installation

The code was tested using Ubuntu 20.04 and Python 3.7/3.8. The software-packages

sudo apt-get install python3-dev
sudo apt-get install build-essential
sudo apt-get install cmake

must be available.

Quick-start

The impatient user can do the following three steps to install the code, all necessary dependencies and run the main script of the energy strategy afterwards:

1. In /home/$USER/ create a folder emb_es_root

mkdir /home/$USER/emb_es_root

2. Go to the folder emb_es_root, clone the repository, and change permissions of the install script chmod +x install.sh and run the script ./install.sh

cd /home/$USER/emb_es_root
git clone https://github.com/TUMFTM/embes
cd embes
chmod +x install.sh
./install.sh

3. To run the main script of the energy strategy, run

cd /home/$USER/emb_es_root
source venv_es/bin/activate
cd embes
export ACADOS_SOURCE_DIR=/home/$USER/emb_es_root/acados
export LD_LIBRARY_PATH=/home/$USER/emb_es_root/acados/lib:$LD_LIBRARY_PATH
python3 main_emb_es.py

Your terminal should display the following information:

Detailed description

Do the following three steps, to check out the necessary code to run the energy strategy. We wish to achieve the following structure:

/home/$USER/emb_es_root
├── acados
└── embes
    ├── external
        └── global_racetrajectory_optimization (submodule of *embedded_energy_strategy*)
    ├── requirements.txt
    └── main_emb_es.py

1. Check out the repository embes and initialize its submodules to receive the necessary powertrain definitions:

git clone https://github.com/TUMFTM/embes
cd embes
git submodule update --recursive --init

2. Download and install the acados template for python into /home/$USER/emb_es_root, since we need it as optimization modeling language. The first lines in the following code fragment to install the template are copied from the acados manual:

cd /home/$USER/emb_es_root
git clone https://github.com/acados/acados.git
cd acados
git submodule update --recursive --init

mkdir -p build
cd build
cmake ..
# add more optional arguments e.g. -DACADOS_WITH_OSQP=OFF/ON -DACADOS_INSTALL_DIR=<path_to_acados_installation_folder> above
make install

3. Set up a virtual environment using virtualenv and install the requirements given in requirements.txt. Insert a name of your choice into <your-venv-name>.

cd /home/$USER/emb_es_root
python3 -m venv <your-venv-name>

Now, activate the virtual environment and upgrade pip

source <your-venv-name>/bin/activate
pip install --upgrade pip

and install the embes/requirements.txt inside

pip3 install /home/$USER/emb_es_root/acados/interfaces/acados_template
wget https://github.com/acados/tera_renderer/releases/download/v0.0.34/t_renderer-v0.0.34-linux
mkdir -p /home/$USER/emb_es_root/acados/bin/
cp t_renderer-v0.0.34-linux /home/$USER/emb_es_root/acados/bin/t_renderer
chmod +x /home/$USER/emb_es_root/acados/bin/t_renderer
cd /home/$USER/emb_es_root/embes
pip install -r requirements.txt

Now you should be able to run the main script starting the energy strategy algorithm within the virtual environment by exporting the library paths to your acados installation:

export ACADOS_SOURCE_DIR=/home/$USER/emb_es_root/acados
export LD_LIBRARY_PATH=/home/$USER/emb_es_root/acados/lib:$LD_LIBRARY_PATH

cd /home/$USER/emb_es_root/embes
python3 main_emb_es.py

To work with the code

To work with the energy-strategy optimization, have a look into the How to use-section of this documentation.